Formulating a Geolocation Bias Correction for DMSP Nighttime Lights of Global Cities
This study examined the potential geolocation biases/errors in the Defense Meteorological Satellite Program (DMSP) nightlight data that has been used in carbon emission models, such as the global high-resolution emission inventory Open-source Data Invento
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, Tomohiro Oda2,3,4
, and Rostyslav Bun1,5(B)
1 Lviv Polytechnic National University, Lviv, Ukraine
[email protected] 2 Universities Space Research Association, Columbia, MD, USA 3 NASA Goddard Space Flight Center, Greenbelt, MD, USA 4 University of Maryland, College Park, MD, USA 5 WSB University, D˛abrowa Górnicza, Poland
Abstract. This study examined the potential geolocation biases/errors in the Defense Meteorological Satellite Program (DMSP) nightlight data that has been used in carbon emission models, such as the global high-resolution emission inventory Open-source Data Inventory for Anthropogenic CO2 (ODIAC). Quantifying and mitigating the bias has become an urgent, critical task in obtaining robust emission estimates for urban areas, the major sources of the global carbon emissions, from nightlight-based emission estimates. We first attempted to characterize the bias by changing the location of urban cores identified by the DMSP data and their orientation using the city boundary data from the Open Street Map (OMS) as a reference. We hypothesized that a geolocation bias-free emission map should maximize the total emission within the administrative boundaries, and developed an iterative optimization algorithm to obtain correction vectors (distance and angle) for shifting emission data (or DMSP data). We implemented the proposed algorithms for many global cities from different continents, and discovered the latitudinal dependence of the bias. The paper is focused on the methodological aspects of the bias correction, and the implementation and applications to global cities. Keywords: Satellite remote sensing data · Night-time lights data · Carbon dioxide emission data · Satellite data bias · Geolocation bias correction
1 Introduction Satellite remote sensing data have been widely used for solving many practical tasks of human activity [1–4]. The satellite remote sensing data for nighttime lights (NTL) from the Defense Meteorological Satellite Program (DMSP) satellites [5–7] has play a major role in this regard. Over the years, DMSP has accumulated a valuable unique long record of NTL data that have been used as a proxy for modeling human world processes [8, 9]. Among many research applications, the DMSP NTL data are used to create highresolution maps (30 × 30 arc-seconds or 1 × 1 km at the equator) of carbon dioxide
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Shakhovska and M. O. Medykovskyy (Eds.): CSIT 2020, AISC 1293, pp. 383–398, 2021. https://doi.org/10.1007/978-3-030-63270-0_25
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(CO2 ) emissions from fossil fuel consumption for energy purposes, specifically Opensource Data Inventory for Anthropogenic CO2 (ODIAC) [10–12]. The global highresolution maps are based on disaggregation of country-level CO2 emission data using large point sources emissions data (especially for electricity generation) and NTL data [10]. The estimation of nonpoint source emission spatial distribution largely depends on the intensity of NTL (DMSP data), whil
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